An algorithm for localizing a sound source with two microphones is introduced and used in real-time situations. This algorithm is inspired by biological computation of interaural time difference as occurring in the barn owl and is a modification of the algorithm proposed by Liu et al. [J. Acoust. Soc. Am.110, 3218–3231 (2001)] in that it creates a three-dimensional map of coincidence location. This eliminates localization artifacts found during tests with the original algorithm. The source direction is found by determining the azimuth at which the minimum of the response in an azimuth-frequency matrix occurs. The system was tested with a pan-tilt unit in real-time in an office environment with signal types ranging from broadband noise to pure tones. Both open loop (pan-tilt unit stationary) and closed loop experiments (pan-tilt unit moving) were conducted. In real world situations, the algorithm performed well for all signal types except pure tones. Subsequent room simulations showed that localization accuracy decreases with decreasing direct-to-reverberant ratio.

Passive sound source localization with sensor arrays is based on the estimation of the time difference of arrival (TDOA), and precise TDOA is required to achieve accurate position estimation. For a majority of practical localization systems (based on TDOA estimation with four sensors in two dimensions), only three time delays are computed to determine the location of interest. This paper presents an approach to determine the position of a manatee by using four hydrophones and all the combinations of the TDOAs available. With four hydrophones, six TDOAs are computed and then combined three by three to get 20 possible points for each position to estimate. Experimental results using the Hilbert envelope peak technique to estimate the TDOAs and the least square method to estimate the position are presented. For the tests conducted it is shown that for a manatee call having a high signal-to-noise ratio, the individual position estimated for each of the 20 combinations of TDOAs lies on a straight line, providing a good estimation of the direction of arrival approximately 85% of the time. However, a good estimation of the position is obtained for a manatee near the hydrophone array approximately 55% of the time.

The use of multiple angle acoustic scatter to discriminate between two taxa of fluid-like zooplankton, copepods and euphausiids, is explored. Using computer modeling, feature extraction, and subsequent classification, the accuracy in discriminating between the two taxa is characterized via computer simulations. The model applies the distorted wave Born approximation together with a simple system geometry, a linear array, to predict a set of noisy training and test data. Three feature spaces are designed, exploiting the relationship between the shape of the scatterer and angularly varying scattering amplitude, to extract discriminant features from these data. Under the assumption of uniform random length and uniform three-dimensional orientation distributions for each class of scatterers, the performance of several classification algorithms is evaluated. Simulations reveal that the incorporation of multiple angle data leads to a marked improvement in classification performance over single angle methods. The improvement is more substantial using broadband scatter. The simulations indicate that under the stated assumptions, a low classification error can be obtained. The use of multiple angle scatter therefore holds promise to substantially improve the in situ acoustic classification of fluid-like zooplankton using simple observation geometries.

Using extensive numerical simulations, several distributed sensorimaging algorithms for localized damage in a structure are analyzed. Given a configuration of ultrasonic transducers, a full response matrix for the healthy structure is assumed known. It is used as a basis for comparison with the response matrix that is recorded when there is damage. Numerical simulations are done with the wave equation in two dimensions. The healthy structure contains many scatterers. The aim is to image point-like defects with several regularly distributed sensors. Because of the complexity of the environment, the recorded traces have a lot of delay spread and travel time migration does not work so well. Instead, the traces are back propagated numerically assuming that there is some knowledge of the background. Since the time at which the back propagated field will focus on the defects is unknown, the Shannon entropy or the bounded variation norm of the image is computed and the time where it is minimal is picked. This imaging method performs well because it produces a tight image near the location of the defects at the time of refocusing. When there are several defects, the singular value decomposition of the response matrix is also carried out.

In the present work, a method of alternating orthogonal projections is described in the context of near-field acoustical holography; it allows missing (or “not measured”) data to be recovered, thus relieving the strictness of measurement requirements related to the use of the discrete Fourier transform. The method described here provides the detailed foundation for the patch holography procedure that has previously been introduced to mitigate finite measurement aperture effects by allowing the sound field to be iteratively extended beyond the measurement aperture. It is also shown that the latter iterative algorithm can be used regardless of the spatial distribution of measured data: i.e., patches can be discontinuous. Numerical simulations performed by using a synthetic sound field created by a point-driven, simply supported plate were used to demonstrate the latter point. In particular, a multipatch holography procedure is described that allows a source distribution to be reconstructed from the hologram pressuremeasured over multiple, unconnected patches. It is finally shown that a related approach allows spatial resolution enhancement by interpolation between measured points.